@article{Huang:2022,
author = {Huang, Yu-Peng and Xia, Yijie and Yang, Lijiang and Wei, Jiachen and Yang, Yi Isaac and Gao, Yi Qin},
title = {SPONGE: A GPU-Accelerated Molecular Dynamics Package with Enhanced Sampling and AI-Driven Algorithms},
journal = {Chinese Journal of Chemistry},
volume = {40},
number = {1},
pages = {160-168},
keywords = {Molecular dynamics, Molecular modeling, Enhanced sampling, Machine learning, Computational chemistry},
doi = {10.1002/cjoc.202100456},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cjoc.202100456},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjoc.202100456},
abstract = {Comprehensive Summary SPONGE (Simulation Package tOward Next GEneration molecular modeling) is a software package for molecular dynamics (MD) simulation of solution and surface molecular systems. In this version of SPONGE, the all- atom potential energy functions used in AMBER MD packages are used by default and other all-atom/coarse- grained potential energy functions are also supported. SPONGE is designed to extend the timescale being approached in MD simulations by utilizing the latest CUDA- enabled graphical processing units (GPU) and adopting highly efficient enhanced sampling algorithms, such as integrated tempering, selective integrated tempering and enhanced sampling of reactive trajectories. It is highly modular and new algorithms and functions can be incorporated con veniently. Particularly, a specialized Python plugin can be easily used to perform the machine learning MD simulation with MindSpore, TensorFlow, PyTorch or other popular machine learning frameworks. Furthermore, a plugin of Finite-Element Method (FEM) is also available to handle metallic surface systems. All these advanced features increase the power of SPONGE for modeling and simulation of complex chemical and biological systems. What is the most favorite and original chemistry developed in your research group? Our research centers at developing methods and theories to unravel molecular mechanisms of chemical and biological systems. By establishing theoretical models, developing enhanced sampling methods combined with machine learning techniques, we are able to conduct comprehensive thermodynamic and dynamic analyses for these complex systems. How do you get into this specific field? Could you please share some experiences with our readers? I got into theoretical chemistry as a PhD student. My PhD adviser Prof. Rudolph A. Marcus led me into this field and inspired me by his love of science. Enjoy life, always learn new things and be independent in thinking are something I learnt from my advisers (Professors Dalin Yang, Qihe Zhu, Rudy Marcus, and Martin Karplus) and would love to pass to my students. How do you supervise your students? We learn from each other. What is the most important personality for scientific research? Curiosity, passion, and persistence have been of great value to my career. What are your hobbies? What's your favorite book(s)? Reading, Ping-Pong, and jogging. I always enjoy reading history. Who influences you mostly in your life? Too many, family, academic advisors, friends, students, and colleagues.},
year = {2022}
}

@article{Abraham:2015,
title = {GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers},
journal = {SoftwareX},
volume = {1-2},
pages = {19-25},
year = {2015},
issn = {2352-7110},
doi = {10.1016/j.softx.2015.06.001},
url = {https://www.sciencedirect.com/science/article/pii/S2352711015000059},
author = {Mark James Abraham and Teemu Murtola and Roland Schulz and Szilárd Páll and Jeremy C. Smith and Berk Hess and Erik Lindahl},
keywords = {Molecular dynamics, GPU, SIMD, Free energy},
abstract = {GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced parallelization algorithms. These work on every level; SIMD registers inside cores, multithreading, heterogeneous CPU–GPU acceleration, state-of-the-art 3D domain decomposition, and ensemble-level parallelization through built-in replica exchange and the separate Copernicus framework. The latest best-in-class compressed trajectory storage format is supported.}
}

@article{Thompson:2022,
title = {LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales},
journal = {Computer Physics Communications},
volume = {271},
pages = {108171},
year = {2022},
issn = {0010-4655},
doi = {10.1016/j.cpc.2021.108171},
url = {https://www.sciencedirect.com/science/article/pii/S0010465521002836},
author = {Aidan P. Thompson and H. Metin Aktulga and Richard Berger and Dan S. Bolintineanu and W. Michael Brown and Paul S. Crozier and Pieter J. {in 't Veld} and Axel Kohlmeyer and Stan G. Moore and Trung Dac Nguyen and Ray Shan and Mark J. Stevens and Julien Tranchida and Christian Trott and Steven J. Plimpton},
keywords = {Molecular dynamics, Materials modeling, Parallel algorithms, LAMMPS}
}   

@article{Maier:2015,
author = {Maier, James A. and Martinez, Carmenza and Kasavajhala, Koushik and Wickstrom, Lauren and Hauser, Kevin E. and Simmerling, Carlos},
title = {ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB},
journal = {Journal of Chemical Theory and Computation},
volume = {11},
number = {8},
pages = {3696-3713},
year = {2015},
doi = {10.1021/acs.jctc.5b00255},
    note ={PMID: 26574453},
URL = { 
        https://doi.org/10.1021/acs.jctc.5b00255
},
eprint = { 
        https://doi.org/10.1021/acs.jctc.5b00255 
}
}

@article{Tian:2020,
author = {Tian, Chuan and Kasavajhala, Koushik and Belfon, Kellon A. A. and Raguette, Lauren and Huang, He and Migues, Angela N. and Bickel, John and Wang, Yuzhang and Pincay, Jorge and Wu, Qin and Simmerling, Carlos},
title = {ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution},
journal = {Journal of Chemical Theory and Computation},
volume = {16},
number = {1},
pages = {528-552},
year = {2020},
doi = {10.1021/acs.jctc.9b00591},
    note ={PMID: 31714766},
URL = { 
        https://doi.org/10.1021/acs.jctc.9b00591
},
eprint = { 
        https://doi.org/10.1021/acs.jctc.9b00591
}
}

@article{MacKerell:1998,
author = {MacKerell, A. D. and Bashford, D. and Bellott, M. and Dunbrack, R. L. and Evanseck, J. D. and Field, M. J. and Fischer, S. and Gao, J. and Guo, H. and Ha, S. and Joseph-McCarthy, D. and Kuchnir, L. and Kuczera, K. and Lau, F. T. K. and Mattos, C. and Michnick, S. and Ngo, T. and Nguyen, D. T. and Prodhom, B. and Reiher, W. E. and Roux, B. and Schlenkrich, M. and Smith, J. C. and Stote, R. and Straub, J. and Watanabe, M. and Wi贸rkiewicz-Kuczera, J. and Yin, D. and Karplus, M.},
title = {All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins},
journal = {The Journal of Physical Chemistry B},
volume = {102},
number = {18},
pages = {3586-3616},
year = {1998},
doi = {10.1021/jp973084f},
    note ={PMID: 24889800},
URL = { 
        https://doi.org/10.1021/jp973084f
},
eprint = { 
        https://doi.org/10.1021/jp973084f 
}
}

@article{Mackerell:2004,
author = {Mackerell Jr., Alexander D. and Feig, Michael and Brooks III, Charles L.},
title = {Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations},
journal = {Journal of Computational Chemistry},
volume = {25},
number = {11},
pages = {1400-1415},
keywords = {ab initio, CMAP, molecular dynamics, dihedral angles, empirical force field, molecular mechanics},
doi = {10.1002/jcc.20065},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.20065},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.20065},
abstract = {Abstract Computational studies of proteins based on empirical force fields represent a powerful tool to obtain structure–function relationships at an atomic level, and are central in current efforts to solve the protein folding problem. The results from studies applying these tools are, however, dependent on the quality of the force fields used. In particular, accurate treatment of the peptide backbone is crucial to achieve representative conformational distributions in simulation studies. To improve the treatment of the peptide backbone, quantum mechanical (QM) and molecular mechanical (MM) calculations were undertaken on the alanine, glycine, and proline dipeptides, and the results from these calculations were combined with molecular dynamics (MD) simulations of proteins in crystal and aqueous environments. QM potential energy maps of the alanine and glycine dipeptides at the LMP2/cc-pVxZ//MP2/6-31G* levels, where x = D, T, and Q, were determined, and are compared to available QM studies on these molecules. The LMP2/cc-pVQZ//MP2/6-31G* energy surfaces for all three dipeptides were then used to improve the MM treatment of the dipeptides. These improvements included additional parameter optimization via Monte Carlo simulated annealing and extension of the potential energy function to contain peptide backbone ϕ, ψ dihedral crossterms or a ϕ, ψ grid-based energy correction term. Simultaneously, MD simulations of up to seven proteins in their crystalline environments were used to validate the force field enhancements. Comparison with QM and crystallographic data showed that an additional optimization of the ϕ, ψ dihedral parameters along with the grid-based energy correction were required to yield significant improvements over the CHARMM22 force field. However, systematic deviations in the treatment of ϕ and ψ in the helical and sheet regions were evident. Accordingly, empirical adjustments were made to the grid-based energy correction for alanine and glycine to account for these systematic differences. These adjustments lead to greater deviations from QM data for the two dipeptides but also yielded improved agreement with experimental crystallographic data. These improvements enhance the quality of the CHARMM force field in treating proteins. This extension of the potential energy function is anticipated to facilitate improved treatment of biological macromolecules via MM approaches in general. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1400–1415, 2004},
year = {2004}
}

@article{Berman:2000,
    author = {Berman, Helen M. and Westbrook, John and Feng, Zukang and Gilliland, Gary and Bhat, T. N. and Weissig, Helge and Shindyalov, Ilya N. and Bourne, Philip E.},
    title = "{The Protein Data Bank}",
    journal = {Nucleic Acids Research},
    volume = {28},
    number = {1},
    pages = {235-242},
    year = {2000},
    month = {01},
    abstract = "{The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.}",
    issn = {0305-1048},
    doi = {10.1093/nar/28.1.235},
    url = {https://doi.org/10.1093/nar/28.1.235},
    eprint = {https://academic.oup.com/nar/article-pdf/28/1/235/9895144/280235.pdf},
}

@article{Sayers:2021,
    author = {Sayers, Eric W and Bolton, Evan E and Brister, J Rodney and Canese, Kathi and Chan, Jessica and Comeau, Donald C and Connor, Ryan and Funk, Kathryn and Kelly, Chris and Kim, Sunghwan and Madej, Tom and Marchler-Bauer, Aron and Lanczycki, Christopher and Lathrop, Stacy and Lu, Zhiyong and Thibaud-Nissen, Francoise and Murphy, Terence and Phan, Lon and Skripchenko, Yuri and Tse, Tony and Wang, Jiyao and Williams, Rebecca and Trawick, Barton W and Pruitt, Kim D and Sherry, Stephen T},
    title = "{Database resources of the national center for biotechnology information}",
    journal = {Nucleic Acids Research},
    volume = {50},
    number = {D1},
    pages = {D20-D26},
    year = {2021},
    month = {12},
    abstract = "{The National Center for Biotechnology Information (NCBI) produces a variety of online information resources for biology, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for the most of these databases. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, RefSeq, SRA, Virus, dbSNP, dbVar, ClinicalTrials.gov, MMDB, iCn3D and PubChem. These resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkab1112},
    url = {https://doi.org/10.1093/nar/gkab1112},
    eprint = {https://academic.oup.com/nar/article-pdf/50/D1/D20/42058080/gkab1112.pdf},
}

@article {Dickson:2014,
	Title = {Lipid14: The Amber Lipid Force Field},
	Author = {Dickson, Callum J and Madej, Benjamin D and Skjevik, Age A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C},
	DOI = {10.1021/ct4010307},
	Number = {2},
	Volume = {10},
	Month = {February},
	Year = {2014},
	Journal = {Journal of chemical theory and computation},
	ISSN = {1549-9618},
	Pages = {865-879},
	URL = {https://europepmc.org/articles/PMC3985482},
}

@article{Wang:2004,
author = {Wang, Junmei and Wolf, Romain M. and Caldwell, James W. and Kollman, Peter A. and Case, David A.},
title = {Development and testing of a general amber force field},
journal = {Journal of Computational Chemistry},
volume = {25},
number = {9},
pages = {1157-1174},
keywords = {general AMBER force field, additive force field, force field parameterization, restrained electrostatic potential (RESP)},
doi = {10.1002/jcc.20035},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.20035},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.20035},
abstract = {Abstract We describe here a general Amber force field (GAFF) for organic molecules. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallographic structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 Å, which is comparable to that of the Tripos 5.2 force field (0.25 Å) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 Å, respectively). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermolecular energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 Å and 1.2 kcal/mol, respectively. These data are comparable to results from Parm99/RESP (0.16 Å and 1.18 kcal/mol, respectively), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to experiment) is about 0.5 kcal/mol. GAFF can be applied to wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1157–1174, 2004},
year = {2004}
}

@InProceedings{ Gowers:2016,
  author    = { {R}ichard {J}. {G}owers and {M}ax {L}inke and {J}onathan {B}arnoud and {T}yler {J}. {E}. {R}eddy and {M}anuel {N}. {M}elo and {S}ean {L}. {S}eyler and {J}an {D}omański and {D}avid {L}. {D}otson and {S}ébastien {B}uchoux and {I}an {M}. {K}enney and {O}liver {B}eckstein },
  title     = { {M}{D}{A}nalysis: {A} {P}ython {P}ackage for the {R}apid {A}nalysis of {M}olecular {D}ynamics {S}imulations },
  booktitle = { {P}roceedings of the 15th {P}ython in {S}cience {C}onference },
  pages     = { 98 - 105 },
  year      = { 2016 },
  editor    = { {S}ebastian {B}enthall and {S}cott {R}ostrup },
  doi       = { 10.25080/Majora-629e541a-00e }
}

@article{Michaud-Agrawal:2011,
author = {Michaud-Agrawal, Naveen and Denning, Elizabeth J. and Woolf, Thomas B. and Beckstein, Oliver},
title = {MDAnalysis: A toolkit for the analysis of molecular dynamics simulations},
journal = {Journal of Computational Chemistry},
volume = {32},
number = {10},
pages = {2319-2327},
keywords = {molecular dynamics simulations, analysis, proteins, object-oriented design, software, membrane systems, Python programming language},
doi = {10.1002/jcc.21787},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.21787},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.21787},
abstract = {Abstract MDAnalysis is an object-oriented library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. It is written in the Python language with some performance-critical code in C. It uses the powerful NumPy package to expose trajectory data as fast and efficient NumPy arrays. It has been tested on systems of millions of particles. Many common file formats of simulation packages including CHARMM, Gromacs, Amber, and NAMD and the Protein Data Bank format can be read and written. Atoms can be selected with a syntax similar to CHARMM's powerful selection commands. MDAnalysis enables both novice and experienced programmers to rapidly write their own analytical tools and access data stored in trajectories in an easily accessible manner that facilitates interactive explorative analysis. MDAnalysis has been tested on and works for most Unix-based platforms such as Linux and Mac OS X. It is freely available under the GNU General Public License from http://mdanalysis.googlecode.com. © 2011 Wiley Periodicals, Inc. J Comput Chem 2011},
year = {2011}
}

@misc{LEaP,
    author = {Pengfei Li and David Cerutti},
    year = {2022},
    title = {Fundamentals of LEaP},
    url = {https://ambermd.org/tutorials/pengfei/index.php}  
}

@article{CGenFF,
author = {Vanommeslaeghe, K. and Raman, E. Prabhu and MacKerell, A. D.},
title = {Automation of the CHARMM General Force Field (CGenFF) II: Assignment of Bonded Parameters and Partial Atomic Charges},
journal = {Journal of Chemical Information and Modeling},
volume = {52},
number = {12},
pages = {3155-3168},
year = {2012},
doi = {10.1021/ci3003649},
note ={PMID: 23145473},
URL = {https://doi.org/10.1021/ci3003649},
eprint = {https://doi.org/10.1021/ci3003649}
}

@misc{pdb2gmx,
    author = {Paul Bauer; Berk Hess; Erik Lindahl},
    year = {2022},
    title = {gmx pdb2gmx},
    url = {https://manual.gromacs.org/documentation/current/onlinehelp/gmx-pdb2gmx.html},
    doi = { 10.5281/zenodo.7037337 }
}

@misc{psfgen,
    author = {Joao V. Ribeiro, Brian Radak, John Stone, Justin Gullingsrud, Jan Saam and Jim Phillips},
    year = {2022},
    title = {VMD psfgen Plugin, Version 2.0},
    url = {https://www.ks.uiuc.edu/Research/vmd/plugins/psfgen/}  
}

@misc{LigParGen,
    author = {Leela S. Dodda, Israel Cabeza de Vaca, Julian Tirado-Rives, Justin Gullingsrud and William L. Jorgensen},
    year = {2022},
    title = {LigParGen Server},
    url = {http://zarbi.chem.yale.edu/ligpargen/}  
}

@misc{rdkit,
    author = {Greg Landrum, Paolo Tosco, Brian Kelley, Ric, sriniker, gedeck, Riccardo Vianello, NadineSchneider, Eisuke Kawashima, David Cosgrove, Andrew Dalke, Dan N, Gareth Jones, Brian Cole, Matt Swain, Samo Turk, AlexanderSavelyev, Alain Vaucher, Maciej Wójcikowski, Ichiru Take, Daniel Probst, Kazuya Ujihara, Vincent F. Scalfani, guillaume godin, Axel Pahl, Francois Berenger, JLVarjo, strets123, JP, DoliathGavid},
    year = {2022},
    title = {rdkit/rdkit: 2022_03_5 (Q1 2022) Release},
    url = {https://zenodo.org/record/6961488#.YvackPhByUk},
    doi = {10.5281/zenodo.6961488}
}

@misc{MindSpore,
    author = {MindSpore},
    year = {2022},
    title = {MindSpore: An Open AI Framwork},
    url = {http://www.mindspore.cn}  
}

@article{pyscf,
author = {Sun,Qiming  and Zhang,Xing  and Banerjee,Samragni  and Bao,Peng  and Barbry,Marc  and Blunt,Nick S.  and Bogdanov,Nikolay A.  and Booth,George H.  and Chen,Jia  and Cui,Zhi-Hao  and Eriksen,Janus J.  and Gao,Yang  and Guo,Sheng  and Hermann,Jan  and Hermes,Matthew R.  and Koh,Kevin  and Koval,Peter  and Lehtola,Susi  and Li,Zhendong  and Liu,Junzi  and Mardirossian,Narbe  and McClain,James D.  and Motta,Mario  and Mussard,Bastien  and Pham,Hung Q.  and Pulkin,Artem  and Purwanto,Wirawan  and Robinson,Paul J.  and Ronca,Enrico  and Sayfutyarova,Elvira R.  and Scheurer,Maximilian  and Schurkus,Henry F.  and Smith,James E. T.  and Sun,Chong  and Sun,Shi-Ning  and Upadhyay,Shiv  and Wagner,Lucas K.  and Wang,Xiao  and White,Alec  and Whitfield,James Daniel  and Williamson,Mark J.  and Wouters,Sebastian  and Yang,Jun  and Yu,Jason M.  and Zhu,Tianyu  and Berkelbach,Timothy C.  and Sharma,Sandeep  and Sokolov,Alexander Yu.  and Chan,Garnet Kin-Lic },
title = {Recent developments in the PySCF program package},
journal = {The Journal of Chemical Physics},
volume = {153},
number = {2},
pages = {024109},
year = {2020},
doi = {10.1063/5.0006074},
URL = {https://doi.org/10.1063/5.0006074},
eprint = {https://doi.org/10.1063/5.0006074}
}

@article{pyscf2,
author = {Sun, Qiming and Berkelbach, Timothy C. and Blunt, Nick S. and Booth, George H. and Guo, Sheng and Li, Zhendong and Liu, Junzi and McClain, James D. and Sayfutyarova, Elvira R. and Sharma, Sandeep and Wouters, Sebastian and Chan, Garnet Kin-Lic},
title = {PySCF: the Python-based simulations of chemistry framework},
journal = {WIREs Computational Molecular Science},
volume = {8},
number = {1},
pages = {e1340},
doi = {10.1002/wcms.1340},
url = {https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1340},
eprint = {https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1340},
abstract = {Python-based simulations of chemistry framework (PySCF) is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, so as to facilitate new method development and enable flexible computational workflows. The package provides a wide range of tools to support simulations of finite-size systems, extended systems with periodic boundary conditions, low-dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure ease of extensibility, PySCF uses the Python language to implement almost all of its features, while computationally critical paths are implemented with heavily optimized C routines. Using this combined Python/C implementation, the package is as efficient as the best existing C or Fortran-based quantum chemistry programs. In this paper, we document the capabilities and design philosophy of the current version of the PySCF package. WIREs Comput Mol Sci 2018, 8:e1340. doi: 10.1002/wcms.1340 This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry},
year = {2018}
}

@article{pyscf3,
author = {Sun, Qiming},
title = {Libcint: An efficient general integral library for Gaussian basis functions},
journal = {Journal of Computational Chemistry},
volume = {36},
number = {22},
pages = {1664-1671},
keywords = {integral, Gaussian type basis, Libcint},
doi = {10.1002/jcc.23981},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.23981},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.23981},
abstract = {An efficient integral library Libcint was designed to automatically implement general integrals for Gaussian-type scalar and spinor basis functions. The library is able to evaluate arbitrary integral expressions on top of p, r and σ operators with one-electron overlap and nuclear attraction, two-electron Coulomb and Gaunt operators for segmented contracted and/or generated contracted basis in Cartesian, spherical or spinor form. Using a symbolic algebra tool, new integrals are derived and translated to C code programmatically. The generated integrals can be used in various types of molecular properties. To demonstrate the capability of the integral library, we computed the analytical gradients and NMR shielding constants at both nonrelativistic and 4-component relativistic Hartree–Fock level in this work. Due to the use of kinetically balanced basis and gauge including atomic orbitals, the relativistic analytical gradients and shielding constants requires the integral library to handle the fifth-order electron repulsion integral derivatives. The generality of the integral library is achieved without losing efficiency. On the modern multi-CPU platform, Libcint can easily reach the overall throughput being many times of the I/O bandwidth. On a 20-core node, we are able to achieve an average output 8.3 GB/s for C60 molecule with cc-pVTZ basis. © 2015 Wiley Periodicals, Inc.},
year = {2015}
}

@article{geometric,
author = {Wang,Lee-Ping  and Song,Chenchen },
title = {Geometry optimization made simple with translation and rotation coordinates},
journal = {The Journal of Chemical Physics},
volume = {144},
number = {21},
pages = {214108},
year = {2016},
doi = {10.1063/1.4952956},
URL = {https://doi.org/10.1063/1.4952956},
eprint = {https://doi.org/10.1063/1.4952956}
}

@article{Jorgensen:1983,
author = {Jorgensen,William L.  and Chandrasekhar,Jayaraman  and Madura,Jeffry D.  and Impey,Roger W.  and Klein,Michael L. },
title = {Comparison of simple potential functions for simulating liquid water},
journal = {The Journal of Chemical Physics},
volume = {79},
number = {2},
pages = {926-935},
year = {1983},
doi = {10.1063/1.445869},
URL = {https://doi.org/10.1063/1.445869},
eprint = {https://doi.org/10.1063/1.445869}
}

@article{Horn:2004,
author = {Horn,Hans W.  and Swope,William C.  and Pitera,Jed W.  and Madura,Jeffry D.  and Dick,Thomas J.  and Hura,Greg L.  and Head-Gordon,Teresa },
title = {Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew},
journal = {The Journal of Chemical Physics},
volume = {120},
number = {20},
pages = {9665-9678},
year = {2004},
doi = {10.1063/1.1683075},
URL = {https://doi.org/10.1063/1.1683075},
eprint = {https://doi.org/10.1063/1.1683075}
}

@article{Izadi:2014,
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@misc{Elliott:2019,
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@misc{ComputationalGraph,
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}
